[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36
[1] 49
[1] 64
[1] 81
[1] 100
Today we will…
map() familyYou must complete the objectives and write up the written components outlined under Section 1 on the Project Details page on Canvas.
Repeating the same operation multiple times.
for() and while()) allow us to iterate.Many operations in R are vectorized.
Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.The error us that if(x > 0) can only be checked for something of length 1 (a single number, not a vector).
Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.if() statements? case_when()pos_neg_zero <- function(x){
state <- case_when(x > 0 ~ "Greater than 0!",
x < 0 ~ "Less than 0!",
.default = "Equal to 0!")
return(state)
}
x <- seq(from = -4, to = 3)
pos_neg_zero(x)[1] "Less than 0!" "Less than 0!" "Less than 0!" "Less than 0!"
[5] "Equal to 0!" "Greater than 0!" "Greater than 0!" "Greater than 0!"
Applying class() to a single variable in a datqaframe returns the data type of that column:
Trying to apply class() to every variable in a dataframe returns the data type of the dataframe:
Write a for() loop…
data_type <- rep(NA, length = ncol(penguins))
for(i in 1:ncol(penguins)){
data_type[i] <- class(penguins[[i]])
}
# format table nicely
tibble(column = names(penguins),
type = data_type) |>
pivot_wider(names_from = column,
values_from = type) |>
knitr::kable() |>
kableExtra::kable_styling(font_size = 30)| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| factor | factor | numeric | numeric | integer | integer | factor | integer |
… but loops are computationally intensive!
We have continually called on across() to reduce repetition.
# A tibble: 1 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<chr> <chr> <chr> <chr> <chr> <chr>
1 factor factor numeric numeric integer integer
# ℹ 2 more variables: sex <chr>, year <chr>
Ugh. Internally, across() uses a for() loop!
for (j in seq_fns) {
fn <- fns[[j]]
out[[k]] <- fn(col, ...)
k <- k + 1L
for() loops?To understand computations in R, two slogans are helpful:
Everything that exists is an object.
Everything that happens is a function call.
John Chambers (creator of the pre-cursor to R)
What’s the big picture?
Note
There are a slew of apply() functions you will likely come across.
We will instead focus on the purrr package and the map() family of functions.
purrrThe purrr package breaks common list manipulations into small, independent pieces.
This strategy involves two steps:
|>.purrr will generalize the solution to every element in the list.A dataframe is a list of columns.
map()The map() function iterates through each item in a supplied list and applies a supplied function.
Note: the first argument in map() is the list, so if we pipe into it, we only specify the function!
map() FamilyThe map_xxx() variants allow you to specify the type of output you want.
map() creates a list.map_chr() creates a character vector.map_lgl() creates an logical vector.map_int() creates a integer vector.map_dbl() creates a numeric vector.All take in a list and a function as arguments.
map() + penguinsCalculate the mean of each column.
bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
43.92193 17.15117 200.91520 4201.75439
~ and .x), just like in across()!Calculate the number of NAs in each column.
map_if()The map_if() function allows us to conditionally apply a function to each item in a list.
# A tibble: 8 × 5
species island bill_length_mm[,1] bill_depth_mm[,1] sex
<fct> <fct> <dbl> <dbl> <fct>
1 Adelie Torgersen -0.883 0.784 male
2 Adelie Torgersen -0.810 0.126 female
3 Adelie Torgersen -0.663 0.430 female
4 Adelie Torgersen NA NA <NA>
5 Adelie Torgersen -1.32 1.09 female
6 Adelie Torgersen -0.847 1.75 male
7 Adelie Torgersen -0.920 0.329 female
8 Adelie Torgersen -0.865 1.24 male
$species
[1] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[8] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[15] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[22] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[29] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[36] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[43] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[50] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[57] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[64] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[71] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[78] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[85] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[92] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[99] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[106] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[113] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[120] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[127] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[134] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[141] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[148] Adelie Adelie Adelie Adelie Adelie Gentoo Gentoo
[155] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[162] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[169] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[176] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[183] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[190] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[197] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[204] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[211] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[218] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[225] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[232] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[239] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[246] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[253] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[260] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[267] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[274] Gentoo Gentoo Gentoo Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo
$island
[1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe
[22] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[29] Biscoe Biscoe Dream Dream Dream Dream Dream
[36] Dream Dream Dream Dream Dream Dream Dream
[43] Dream Dream Dream Dream Dream Dream Dream
[50] Dream Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[57] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[64] Biscoe Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen
[71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[85] Dream Dream Dream Dream Dream Dream Dream
[92] Dream Dream Dream Dream Dream Dream Dream
[99] Dream Dream Biscoe Biscoe Biscoe Biscoe Biscoe
[106] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[113] Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen Torgersen
[120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream
[134] Dream Dream Dream Dream Dream Dream Dream
[141] Dream Dream Dream Dream Dream Dream Dream
[148] Dream Dream Dream Dream Dream Biscoe Biscoe
[155] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[162] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[169] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[176] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[183] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[190] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[197] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[204] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[211] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[218] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[225] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[232] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[239] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[246] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[253] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[260] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[267] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[274] Biscoe Biscoe Biscoe Dream Dream Dream Dream
[281] Dream Dream Dream Dream Dream Dream Dream
[288] Dream Dream Dream Dream Dream Dream Dream
[295] Dream Dream Dream Dream Dream Dream Dream
[302] Dream Dream Dream Dream Dream Dream Dream
[309] Dream Dream Dream Dream Dream Dream Dream
[316] Dream Dream Dream Dream Dream Dream Dream
[323] Dream Dream Dream Dream Dream Dream Dream
[330] Dream Dream Dream Dream Dream Dream Dream
[337] Dream Dream Dream Dream Dream Dream Dream
[344] Dream
Levels: Biscoe Dream Torgersen
$bill_length_mm
[,1]
[1,] -0.88320467
[2,] -0.80993901
[3,] -0.66340769
[4,] NA
[5,] -1.32279862
[6,] -0.84657184
[7,] -0.91983750
[8,] -0.86488825
[9,] -1.79902541
[10,] -0.35202864
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[12,] -1.12131806
[13,] -0.51687637
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[156,] 1.11328455
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[232,] 0.23409663
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[267,] 0.41726078
[268,] 2.04742170
[269,] 0.10588173
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[272,] NA
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[294,] 2.57859773
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[340,] 2.17563660
[341,] -0.07728242
[342,] 1.04001889
[343,] 1.25981586
[344,] 1.14991738
attr(,"scaled:center")
[1] 43.92193
attr(,"scaled:scale")
[1] 5.459584
$bill_depth_mm
[,1]
[1,] 0.78430007
[2,] 0.12600328
[3,] 0.42983257
[4,] NA
[5,] 1.08812936
[6,] 1.74642615
[7,] 0.32855614
[8,] 1.24004400
[9,] 0.48047078
[10,] 1.54387329
[11,] -0.02591137
[12,] 0.07536506
[13,] 0.22727971
[14,] 2.05025544
[15,] 1.99961722
[16,] 0.32855614
[17,] 0.93621471
[18,] 1.79706436
[19,] 0.63238542
[20,] 2.20217008
[21,] 0.58174721
[22,] 0.78430007
[23,] 1.03749114
[24,] 0.48047078
[25,] 0.02472685
[26,] 0.88557650
[27,] 0.73366185
[28,] 0.37919435
[29,] 0.73366185
[30,] 0.88557650
[31,] -0.22846423
[32,] 0.48047078
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attr(,"scaled:scale")
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[151] female male female male female male male female female male
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[211] female male female male female male female male <NA> male
[221] female male female male male female female male female male
[231] female male female male female male female male female male
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[251] female male female male female male <NA> male female male
[261] female male female male female male female male <NA> male
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[291] female male male female female male female male female male
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[311] male female female male female male male female male female
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[287,] -1.25748435
[288,] -1.25748435
[289,] -1.25748435
[290,] -1.25748435
[291,] -1.25748435
[292,] -1.25748435
[293,] -1.25748435
[294,] -1.25748435
[295,] -1.25748435
[296,] -1.25748435
[297,] -1.25748435
[298,] -1.25748435
[299,] -1.25748435
[300,] -1.25748435
[301,] -1.25748435
[302,] -1.25748435
[303,] -0.03552216
[304,] -0.03552216
[305,] -0.03552216
[306,] -0.03552216
[307,] -0.03552216
[308,] -0.03552216
[309,] -0.03552216
[310,] -0.03552216
[311,] -0.03552216
[312,] -0.03552216
[313,] -0.03552216
[314,] -0.03552216
[315,] -0.03552216
[316,] -0.03552216
[317,] -0.03552216
[318,] -0.03552216
[319,] -0.03552216
[320,] -0.03552216
[321,] 1.18644003
[322,] 1.18644003
[323,] 1.18644003
[324,] 1.18644003
[325,] 1.18644003
[326,] 1.18644003
[327,] 1.18644003
[328,] 1.18644003
[329,] 1.18644003
[330,] 1.18644003
[331,] 1.18644003
[332,] 1.18644003
[333,] 1.18644003
[334,] 1.18644003
[335,] 1.18644003
[336,] 1.18644003
[337,] 1.18644003
[338,] 1.18644003
[339,] 1.18644003
[340,] 1.18644003
[341,] 1.18644003
[342,] 1.18644003
[343,] 1.18644003
[344,] 1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559
# A tibble: 8 × 5
species island bill_length_mm[,1] bill_depth_mm[,1] sex
<fct> <fct> <dbl> <dbl> <fct>
1 Adelie Torgersen -0.883 0.784 male
2 Adelie Torgersen -0.810 0.126 female
3 Adelie Torgersen -0.663 0.430 female
4 Adelie Torgersen NA NA <NA>
5 Adelie Torgersen -1.32 1.09 female
6 Adelie Torgersen -0.847 1.75 male
7 Adelie Torgersen -0.920 0.329 female
8 Adelie Torgersen -0.865 1.24 male
How can we print out Timothee Chalamet’s statistics song?
[1] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
[5] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
[9] "Statistics, yup" "Statistics, yup"
Let’s turn this into a function!
The glue package embeds R expressions in curly brackets that are then evaluated and inserted into the argument string.
99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around, 98 bottles of beer on the wall…
bottles_lyrics <- function(n){
lyrics <- glue::glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
return(lyrics)
}
bottles_lyrics(3)3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
bottles_song <- function(n){
song <- map_chr(n:0, bottles_lyrics)
return(glue::glue("{song}"))
}
bottles_song(3)3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer
Take one down, pass it around, 0 bottles of beer on the wall
0 bottles of beer on the wall, 0 bottles of beer
Take one down, pass it around, -1 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer.
Go to the store and buy some more, 99 bottles of beer on the wall…
bottles_lyrics <- function(n){
if(n == 0){
lyrics <- glue::glue("No more bottles of beer on the wall, no more bottles of beer. \nGo to the store and buy some more, 99 bottles of beer on the wall")
} else{
lyrics <- glue::glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
}
return(lyrics)
}4 bottles of beer on the wall, 4 bottles of beer
Take one down, pass it around, 3 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer
Take one down, pass it around, 0 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer.
Go to the store and buy some more, 99 bottles of beer on the wall
Today we will…
case_when()pmap()dplyr 1.1.0 – case_when(.default = )new_var <- case_when(condition1 ~ change1,
condition2 ~ change2,
.default = variable)
condition should result in a TRUE/FALSE Boolean valuechange is some sort of modification to a variableTip
Tip: Test it out on a simple case, then generalize!
Works with older versions of dplyr – will always work!
pos_neg_zero <- function(x){
stopifnot(is.numeric(x))
state <- case_when(x > 0 ~ "Greater than 0!",
x < 0 ~ "Less than 0!",
TRUE ~ "Equal to 0!")
return(state)
}
x <- seq(from = -4, to = 4, by = 1)
pos_neg_zero(x)[1] "Less than 0!" "Less than 0!" "Less than 0!" "Less than 0!"
[5] "Equal to 0!" "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"
Works with dplyr versions 1.1.0 over newer.
pos_neg_zero <- function(x){
stopifnot(is.numeric(x))
state <- case_when(x > 0 ~ "Greater than 0!",
x < 0 ~ "Less than 0!",
.default = "Equal to 0!")
return(state)
}
x <- seq(from = -4, to = 4, by = 1)
pos_neg_zero(x)[1] "Less than 0!" "Less than 0!" "Less than 0!" "Less than 0!"
[5] "Equal to 0!" "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"
map2() familyThese functions allow us to iterate over two lists at the same time.
map2()map2_chr()map2_lgl()map2_int()map2_dbl()pmap()These have two list arguments, denoted x and y.
pmap() doing?sing_day() functionTip
Start with a small example and test it out on a single day outside of the function environment.
xmas2 |>
pull(Full.Phrase)
The function glue_collapse() might be useful – look at the arguments in the R documentation.
Iterate your sing_day() function over a set of days
Style your output!
Comments from Lab 7: Functions and Fish
Get creative with your plots…
How did I figure out the colors?